Systems and methods for identifying subsurface hydrogen accumulation

Machine learning and remote sensing are used to identify surface features indicative of subsurface hydrogen accumulations, addressing the inefficiencies in current exploration methods by characterizing hydrogen SMaRTS components and detecting active production zones, thereby improving the economic viability of hydrogen production.

JP2025114660AActive Publication Date: 2025-08-05OHIO STATE INNOVATION FOUND
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Patent Information

Application Number
JP2025075474
Authority / Receiving Office
JP · JP
Patent Type
Applications
Current Assignee / Owner
Priority Date
2021-04-30
Filing Date
2025-04-30
Publication Date
2025-08-05
Estimated Expiration
2042-04-29

AI Technical Summary

Technical Problem

Current subsurface exploration for natural hydrogen is limited by inadequate understanding of geological conditions and lacks effective methods to identify suitable locations for hydrogen production and retention, leading to inefficient and economically unsound drilling strategies.

Method used

Application of machine learning and remote sensing techniques to identify aboveground surface features associated with subsurface hydrogen accumulations, using a SMaRTS model to characterize hydrogen source, reservoir, trap, and seal components, and employing high-resolution imagery to detect dynamic ovoid features indicative of active hydrogen production.

Benefits of technology

Enables systematic identification of potential hydrogen production zones, reducing unnecessary drilling efforts and enhancing the efficiency and economic viability of hydrogen exploration and production.

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Abstract

SOLUTION: Systems, apparatuses, methods and computer program products are disclosed for training an image analysis engine to identify surface features of the Earth consistent with subsurface hydrogen accumulation. An example method includes receiving, by communications circuitry, a training dataset of labeled images illustrating surface features consistent with subsurface hydrogen accumulation, where the surface features consistent with subsurface hydrogen accumulation include ovoid surficial depressions. The example method further includes training, by a model generator and using the training dataset, an image classification model of the image analysis engine to identify whether new images contain surface features consistent with subsurface hydrogen accumulation. The example method further includes hosting the image classification model by the image analysis engine.EFFECT: The present invention applies machine learning and remote sensing to automatically identify geomorphological features that are consistent with subsurface hydrogen accumulation.SELECTED DRAWING: Figure 1
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